Mirror Neuronsand Human-robot Interaction in Assembly Cells

Anthropomorphic or non-anthropomorphic? Effects of biological sex in observation of actions in a digital human model and a gantry robot model

Robots are ever more relevant for everyday life, such as healthcare or rehabilitation, as well as for modern industrial environment. One important issue in this context is the way we perceive robots and their actions. From our previous study, evidence exists that sex can affect the way people perceive certain robot's actions. In our fMRI study, we analyzed brain activations of female and male participants, while they observed anthropomorphic and robotic movements performed by a human or a robot model. While lying in the scanner, participants rated the perceived level of anthropomorphic and robotic likeness of movements in the two models. The observation of the human model and the anthropomorphic movements similarly activated the biological motion coding areas in posterior temporal and parietal areas. The observation of the robot model activated predominantly areas of the ventral stream, whereas the observation of robotic movements activated predominantly the primary and higher order motor areas. To note, this later activation originated mainly from female participants, whereas male participants activated, in both robot model and robotic movements contrasts, areas in the posterior parietal cortex. Accordingly, the general contrast of sex suggests that men tend to use the ventro-dorsal stream most plausibly to rely on available previous knowledge to analyze the movements, whereas female participants use the dorso-dorsal and the ventral streams to analyze online the differences between the movement types and between the different models. The study is a first step toward the understanding of sex differences in the processing of anthropomorphic and robotic movements.

Tracking Anthropomorphizing Behavior in Human-Robot Interaction

Existing methodologies to describe anthropomorphism in human-robot interaction often rely either on specific one-time responses to robot behavior, such as keeping the robot's secret, or on post hoc measures, such as questionnaires. Currently, there is no method to describe the dynamics of people's behavior over the course of an interaction and in response to robot behavior. In this paper, I propose a method that allows the researcher to trace anthropomorphizing and non-anthropomorphizing responses to robots dynamically moment-by-moment over the course of human-robot interactions. I illustrate this methodology in a case study and find considerable variation between participants, but also considerable intrapersonal variation in the ways the robot is anthropomorphized. That is, people may respond to the robot as if it was another human in one moment and to its machine-like properties in the next. These findings may influence explanatory models of anthropomorphism.

Gender Effects in Observation of Robotic and Humanoid Actions

Robots are gaining an increasingly important role in industrial production. Notably, a high level of acceptance is an important factor for co-working situation between human and robot. The aim of the present study was to investigate the differences in the perception of anthropomorphic and robotic movements using models consisting of a virtual robot and a digital human. Videos of each model displayed different degrees of human likeness or robot likeness in speed and trajectories of placing movements. Female and male participants were asked to rate on a Likert scale the perceived levels of human likeness or robot likeness in the two models. Overall, results suggest that males were sensitive to the differences between robotic and anthropomorphic movements, whereas females showed no difference between them. However, compared to males, female participants attributed more anthropomorphic features to robotic movements. The study is a first step toward a more comprehensive understanding of the human ability to differentiate between anthropomorphic and robotic movements and suggests a crucial role of gender in the human-robot interaction.

Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents.